• Deep Semantic Segmentation of Mammographic Images

    Deep Semantic Segmentation of Mammographic Images MIAS and INbreast are mammographic datasets for the detection and diagnosis of breast cancer. With the dawn of digital mammograms, one important preprocessing step for the tasks of detection and diagnosis is the removal of the pectoral muscle and background area from the images, therefore this task has been tackled […]

  • Contextual descriptors for superpixel-based segmentation

    Land-cover maps are one of the main sources of information for studies that support the creation of public policies in areas like urban planning and environmental monitoring. Their automatic creation involves learning to annotate all samples of a Remote Sensing Image (RSI) from just a few annotated by the user. Nevertheless, low-level descriptors like color […]

  • Natural disaster monitoring

    Natural disaster monitoring is a fundamental task to create prevention strategies, as well as to help authorities to act in the control of damages, coordinate rescues, and help victims.
    Among all natural disasters, flooding is possibly the most extensive and devastating one being considered as the world’s most costly type of natural hazard in terms of both economic losses and human causalities […]

  • Multiscale classification of remote sensing images

    A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multiscale […]

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